Predictive maintenance: a comparison of costs and benefits
Predictive maintenance saves costs and reduces downtime – companies can cut maintenance costs by 25-30% and reduce machine downtime by 70-75%. At the same time, the service life of machines is extended by 20-40%. But how do predictive and preventive approaches compare directly? Here are the most important points:
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Predictive maintenance uses real-time monitoring and AI to detect problems at an early stage. Initial investment: €50,000-500,000. ROI: 12-24 months.
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Preventive maintenance is based on fixed intervals and scheduled checks. Initial investment: € 25,000-200,000. ROI: 6-12 months.
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Results: Predictive systems offer greater savings and efficiency, but require specialized technologies and personnel.
Quick Comparison
|
Aspect |
Predictive maintenance |
Preventive maintenance |
|---|---|---|
|
Cost reduction |
25-30% |
8-12% |
|
Failure reduction |
70-75% |
10-20% |
|
Initial investment |
50.000-500.000 € |
25.000-200.000 € |
|
Energy saving |
7-12% |
5-11% |
|
ROI period |
12-24 months |
6-12 months |
Conclusion: Predictive maintenance offers greater long-term benefits, while preventive approaches are quicker and cheaper to implement. The choice depends on the company’s objectives and resources. There are different opportunities for machine operators and manufacturers.
Maintenance 4.0 for intralogistics - less downtime, less maintenance
This example from Schaeffler from 2018 in its then newest logistics center shows how intelligent maintenance of the mission-critical units was implemented together with SSI Schaefer as a provider of intralogistics solutions.
1. predictive maintenance systems
Predictive maintenance systems combine various technologies to enable long-term savings.
Here are the main components of such a system in detail:
Core components of a predictive system
-
Sensors and data acquisition
Sensors measure important parameters such as vibration, temperature and pressure in real time. The costs for installation in a medium-sized production plant are between 50,000 and 200,000 euros [1]. -
Data analysis software and AI
Software solutions for analysis and visualization cost between 30,000 and 150,000 euros. They use machine learning to identify patterns and predict failures at an early stage [3].
|
Cost component |
Typical investment |
|---|---|
|
Sensors & Hardware |
50.000 – 200.000 € |
|
Software & analysis tools |
30.000 – 150.000 € |
|
Integration & Installation |
20.000 – 100.000 € |
|
Employee training |
10.000 – 50.000 € |
The budgets relate to machine operators. However, this is precisely where the opportunities lie for manufacturers of machines and systems: if these components are already available on the system side, additional benefits and sales can be generated. This supports differentiation and smart service offerings on the part of manufacturers.
Practical examples and challenges
A paper manufacturer invested 650,000 euros in a predictive maintenance system and was able to reduce unplanned downtime by 70%. The investment paid for itself in just 5 months, with annual savings of 1.5 million euros [7].
A chemical plant had to invest an additional 50,000 euros in a data standardization layer to solve compatibility problems between older systems and the new analysis software [8]. These adaptations are often crucial for success.
Key performance indicators
In order to measure the success of such systems, certain key figures should be continuously monitored:
- OEE (Overall Equipment Effectiveness)
- MTBF (Mean Time Between Failures)
- Maintenance costs in relation to system value
- Ratio of planned to unplanned maintenance
A pilot project on critical systems can help to evaluate the ROI before the system is rolled out company-wide.
2. preventive maintenance methods
Having looked at predictive systems, let’s now take a look at classic preventive maintenance. These methods are characterized by clearly defined and structured processes, but are less flexible when it comes to detecting faults at an early stage.
Preventive maintenance ensures planned and regular maintenance. However, the introduction of such systems requires careful planning and investment.
|
Maintenance type |
Main features |
Typical application |
|---|---|---|
|
Time-based |
Fixed intervals |
Standardized production lines |
|
Usage-based |
After operating hours |
Heavy machines |
|
State-based |
Regular inspections |
Critical systems |
|
Risk-based |
Prioritization according to criticality |
Safety-relevant systems |
Economic impact
Coca-Cola FEMSA provides an impressive example: at its plant in Jundiaí, unplanned downtime was reduced by 40%. At the same time, the overall equipment effectiveness (OEE) increased from 85% to 92% and 1.2 million euros were saved annually [4]. Preventive measures have also had the following effects:
- Maintenance costs reduced by 12-18%
- Decrease in energy consumption by 5-11
- Reduction in MRO inventories by 19-23% [1]
Costs and implementation
The introduction of a preventive maintenance system is associated with various investments:
- CMMS software: 5,000 to 50,000 euros
- Employee training: 1,000 to 5,000 euros per employee
- Test equipment: 10,000 to 100,000 euros
- Consulting services: 10,000 to 50,000 euros [2]
These costs reflect the operator’s perspective. They are crucial for evaluating the advantages and limitations of preventive methods compared to predictive approaches.
Integration challenges
The switch to preventive maintenance brings with it a number of challenges. These include extensive training, integrating new systems and adjusting production schedules. Nevertheless, the savings can be considerable: preventive maintenance costs an average of 13 euros per horsepower per year, while reactive maintenance costs 24 euros – a saving of 45% [5]. Machine manufacturers can also take advantage of these benefits: corresponding smart service packages, pre-integrated with their own machines and easy to use by the operator, are new sources of revenue and increase customer loyalty.
These figures and challenges play a central role in the choice between preventive and predictive maintenance strategies. A direct comparison of the two approaches follows in the next section.
"Maximum machine availability, minimum downtimes - with predictive maintenance, service calls can be minimized and costs sustainably reduced. It's a win-win for machine manufacturers and operators."
Direct comparison: advantages and disadvantages
The comparison between predictive and preventive maintenance shows clear differences in terms of costs, efficiency and implementation. The following table summarizes the most important aspects and highlights the respective strengths and weaknesses. A practical example illustrates these differences:
|
Aspect |
Predictive maintenance |
Preventive maintenance |
|---|---|---|
|
Cost reduction |
25-30%[5] |
Lower savings |
|
Failure reduction |
70-75% |
10-20% |
|
Initial investment |
50.000-500.000 € |
25.000-200.000 € |
|
Energy saving |
7-12% |
Lower savings |
|
Personnel qualification |
Data scientists, IoT specialists |
Classic maintenance technicians |
|
ROI period |
12-24 months |
6-12 months |
A practical example: Siemens Power Generation has introduced an AI-supported forecasting system for gas turbines. This has reduced unplanned downtime by 70%, resulting in annual savings of around €5.5 million [10].
Technological requirements
Preventive maintenance systems usually rely on simple planning software and basic measurement technology. In contrast, predictive maintenance requires a more complex infrastructure, including IoT sensors, big data systems, AI analysis tools and networked communication systems. In this area in particular, machine manufacturers with a large installed base may be predestined compared to smaller or medium-sized machine operators.
However, technology alone is not enough – the right personnel are also crucial.
Personnel requirements and training
A common problem with the introduction of predictive maintenance is the lack of qualified personnel. According to a survey of manufacturers, 37% of companies see this as the biggest challenge[1]. However, investing in training can lead to more efficient processes in the long term. These trainings can in turn be an offer in the service portfolio for manufacturers.
Scalability and efficiency
An automotive supplier has shown how scalable predictive maintenance can be: The system was implemented in 30 plants within 18 months[8]. The data-based approach improves the use of resources, reduces energy consumption, extends the service life of machines and reduces spare parts inventories.
The decision between predictive and preventive maintenance should be based on a company’s specific requirements and its long-term goals.
From the machine manufacturers’ point of view, their own target markets and customer structure play a role in the extent to which and which smart service offerings, from preventive to predictive, can be marketed.
Key findings and recommendations
The analysis provides clear indications for optimization: industry-specific ROI ratios and strategies for implementation that can be derived directly from the market data.
ROI by sector
|
Industry |
ROI ratio |
Main advantages |
|---|---|---|
|
Aviation |
10:1 or higher |
Fulfillment of safety requirements |
|
Oil & Gas |
8:1 to 12:1 |
Managing complex logistics, avoiding high downtime costs |
|
Manufacturing |
3:1 to 5:1 |
Increasing efficiency in production lines |
Strategies for implementation according to company size
For large companies: A phased approach has proven effective. At the Siemens plant in Congleton, the use of sensors and AI analysis has enabled engine failures to be detected 36 hours in advance. The result: 100 hours less downtime per year and annual savings of £100,000 [10].
For medium-sized companies: Cloud-based solutions offer a cost-effective entry point. A medium-sized printing company implemented a vibration measurement system for less than €10,000 and reduced unplanned downtime by 15% within six months [11].
For machine manufacturers, it is not so much their own size, but rather their customer structure, their own installed base and their ability to market a service portfolio that are decisive.
Success factors for implementation
Important KPIs for evaluating the effects:
- Overall equipment effectiveness (OEE)
- Average time between failures (MTBF)
- Maintenance costs in relation to system value
- Ratio of planned to unplanned maintenance
An example from the energy sector shows that predictive maintenance on turbine generators was able to reduce costs by 35% within three years [8].
Technological development and prospects
New technologies such as edge computing and digital twins open up further opportunities to increase efficiency. Studies predict that AI-supported predictive maintenance can reduce costs by a further 10-15% [11]. This progress illustrates the importance of a forward-looking strategy.
Practical recommendations
The following measures are based on the facts:
- Identify critical equipment: Focus on machines with high financial risk.
- Investment in sensor technology: High-quality sensors and reliable data acquisition are crucial.
- Employee training: Early training makes it easier to deal with new technologies.
- Pay attention to system compatibility: Pay attention to expandability and integration into existing systems.
A thorough analysis of your own requirements is the key. With clear goals and well-planned implementation, companies can benefit regardless of their size. Machine manufacturers would do well to take these requirements into account in their products and, above all, in their service portfolio.
FAQs
The following FAQs provide a quick overview of the financial benefits and savings that can be achieved through predictive maintenance in the manufacturing industry.
What are the typical cost savings from predictive maintenance?
Predictive maintenance significantly reduces maintenance costs compared to conventional approaches. An overview of possible savings:
|
Maintenance type |
Savings potential |
Main advantages |
|---|---|---|
|
Preventive maintenance |
8-12% |
Better planning of maintenance intervals |
|
Reactive maintenance |
Up to 40% |
Avoidance of unplanned downtimes |
|
Inspection costs |
25% |
More efficient use of resources |
For example, a manufacturing company reduced its annual maintenance costs from €1 million to €880,000 – a saving of around 12% [1]. The following improvements were also documented:
- Reduction of downtimes according to the core analyses [4]
- Production increase of 20-25% [4]
- Reduce energy costs by 5-15% through optimized machine performance
- Less maintenance hours and reduced personnel deployment by 20-50 [1]
- More efficient spare parts management, reducing storage costs by up to 30% [8]
- Longer machine service life, as confirmed in the core analyses [2]
The amortization period for predictive maintenance systems is usually 12-24 months [8]depending on the size of the systems and the complexity of the implementation.
These results clearly show how predictive maintenance helps companies to reduce costs and increase efficiency.
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